Prioritization of High-Risk Location Remediation for Road Accident Mitigation with an Economic Perspective (Case Study: Arak-Salafchegan Route)

Document Type : Original Article

Authors
Kamyar Saberi 1
Mahmoud Ameri 2
Barat Mojaradi 3
MohammadReza Khakbaz 4
1 M.Sc., Grad., Department of Civil Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
2 Professor, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.
3 Associate Professor, Professor, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.
4 Ph.D., Student, Department of Civil Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
10.22034/road.2026.552821.2441
Abstract
Road accidents pose a significant challenge to global transportation systems, particularly in developing countries where accident-prone areas substantially contribute to increased fatalities and economic losses. This study employed an integrated approach combining Bayesian modeling and the TOPSIS multi-criteria decision-making method to identify and prioritize high-risk zones along the Arak-Salafchegan route. Using two years of accident data, road geometric characteristics, weather conditions, and traffic factors, we applied Bayesian analysis to assess accident risks across different road segments, followed by safety and economic-based prioritization of critical points. Our findings reveal intersection density, proximity to residential/industrial areas, and driver inattention as the most significant risk factors. The study area was divided into 15 segments, with segments 9, 15, 1-2, and 11 identified as top priorities for safety interventions. Adverse weather conditions like fog were found to exacerbate accident risks. The research underscores the need for establishing a comprehensive accident database, incorporating cost-benefit analysis into decision-making, and implementing targeted safety measures. By integrating advanced statistical and multi-criteria methods, this study provides a scientifically robust framework for road safety improvement that can be adapted to other high-risk routes.
Keywords
Road accidents
accident-prone spots
safety
accident prediction
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